Improving Social Bot Detection Through Aid and Training.

Area of Science:

• Human-computer interaction
• Social media analysis
• Computational social science

Background:

• Social bots on platforms like Twitter can facilitate online deception.
• Effective detection of social bots is crucial for mitigating misinformation and manipulation.
• Previous research has explored various methods for bot detection, but user-centric interventions require further investigation.

Purpose of the Study:

• To evaluate the effectiveness of three distinct aids in improving individuals' ability to detect social bots.
• To compare the impact of a bot indicator score, a training video, and a warning on user judgment accuracy.
• To provide an experimental framework for assessing interventions against social bot vulnerability.

Main Methods:

• A simulated social media task involving 60 Twitter personas was used.
• Participants judged personas as human or social bot, with bot probability estimated using machine learning algorithms.
• Two experiments compared control groups with intervention groups receiving bot indicator scores, a training video, or a warning.

Main Results:

• The bot indicator score intervention significantly enhanced predictive performance and reduced overconfidence.
• A training video also proved effective in improving bot detection, though to a lesser extent than the score.
• A warning intervention did not significantly impact participants' ability to detect social bots.

Conclusions:

• Informative interventions, such as bot scores and training, demonstrably improve social bot detection.
• Warning users about social bots alone is insufficient to enhance detection capabilities.
• The study highlights the value of specific interventions for reducing user vulnerability to social bots.